Using information obtained through informetrics to address practical problems and to aid decision making
Bibliographic record
Abstract
Abstract Sponsored by: ASIS&T SIG/MET This panel aims to inform participants of, and to stimulate interest in, the diverse ways in which the measurement of information (informetrics) is used in real‐world applications. Its timeliness is indicated by the recent increase in interest amongst ASIS&T members in informetrics that culminated in the endorsement of the change of status of SIG/MET from a virtual SIG to fully functional ASIS&T SIG. The panelists, selected for their diverse experiences in informetrics, address a diversity of issues in the use of informetric information in real‐world situations. The format of the panel is designed to encourage audience participation with the audience being encouraged to suggest issues for discussion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.015 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".